Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.
Top1 Introduction
Formula One data has a dynamic nature (Aigner, et al., 2011), it also consists of event-based data and it shows various differences when observing it from the drivers’ perspectives. Moreover, inspecting the data between different time intervals might lead to the detection of several temporal stages in a race, for example, lap by lap, but also between arbitrarily identified race phases. Lots of outside distractions play a crucial role for the identified dynamic race patterns, being it weather conditions, accidents, bad performing cars, or drivers that behave not equally well supposed the race conditions change from time to time.
In this book chapter a look is taken at real-world Formula One data that is acquired on a lap basis for all participating drivers. Getting an overview about such data on a temporally aligned comparable ground truth is important, however, a calendar-based visualization (van Wijk and van Selow, 1999) for overview purposes about all races having taken place so far (see Figure 1) is also required to guide the data exploration process as well as to select certain races, normal as well as abnormal ones. Apart from just inspecting a static line plot for the temporal lap time, driver-by-driver, plenty of interaction techniques (Yi, et al., 2007) are supported to stepwisely analyze the data on the provided data dimensions.
First the structure and the types of data accessible from a Formula One database are taken into account. As a second step simple, well-known, and easy-to-understand visualization techniques designed for the non-experts in information visualization are designed and presented, and finally, a visual classification of the found properties based on events, drivers, and time periods is described. This work is an extended version from an already published paper (Lampprecht, et al., 2019) in which the focus was more on the visual classification of the time-dependent Formula One data. Here, the following visual classification aspects were added to further strengthen the original work:
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Events: These might be caused by special aspects concerning weather conditions, car problems, safety car phases, pit stops, and many more. Such events typically have a large impact on the dynamic visual patterns.
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Drivers: Those can be classified in several ways, for example, bad, good, or excellent drivers, either on previous race results or warm-up races before the actual race takes place. Moreover, drivers might have health issues, they might be in special environments, or general conditions apply that might also change during a race.
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Time Periods: There are several time-based classifications, for example, classified into begin, middle, or towards the end of a race. Moreover, they might be classified into slow, medium, or fast time periods. Finally, the dynamic visual patterns might be used to identify time periods during Formula One races, for example, caused by safety car phases.
To further extend the original work, a more thorough and more detailed application example than in the original work, now taking the visual classification into account, is described. The focus is on all available Formula One races available in the provided database, also the new ones that have not been considered in the original work. To reach the classification goal, a dynamic race position diagram and a lap times line plot is used to show the varying positions during the race with lap time information. Finally, scalability issues and limitations of the web-based visualization tool are discussed.
Figure 1.
As an overview all races from 2004 to 2018 are presented in a green color coding. The color indicates the fastest lap of each race in milliseconds (darker green visually encodes faster laps).